class_docstr: Search implementation of :class:`~nni.nas.strategy.RandomOneShot`.
  
  Do not need extra preprocessing of dataloader.
  
  See Also
  --------
  nni.nas.strategy.RandomOneShot
  nni.nas.pytorch.oneshot.base_lightning.BaseOneShotLightningModule
docstr: Export of Random one-shot. It will return an arbitrary architecture.
docstr_tok: ["Export","of","Random","one-shot",".","It","will","return","an","arbitrary","architecture","."]
code: def export(self) -> dict[str, Any]:
      """
      Export of Random one-shot. It will return an arbitrary architecture.
      """
      warnings.warn(
          "Direct export from RandomOneShot returns an arbitrary architecture. "
          "Sampling the best architecture from this trained supernet is another search process. "
          "Users need to do another search based on the checkpoint of the one-shot strategy.",
          UserWarning,
      )
      return super().export()
  
imports: from __future__ import annotations
  import warnings
  import logging
  from typing import Any, Callable, TYPE_CHECKING
  import pytorch_lightning
  import torch
  from nni.mutable import Sample
  from nni.nas.nn.pytorch import ModelSpace
  from .base_lightning import BaseOneShotLightningModule
  from .profiler import ProfilerPenalty, ExpectationProfilerPenalty, SampleProfilerPenalty
label_desc: Use image node_id 7 for calling the RandomSamplingLightningModule obj's underlying member method code with example usage: obj.export() and returns: super